Overview

Dataset statistics

Number of variables63
Number of observations157800
Missing cells0
Missing cells (%)0.0%
Duplicate rows43
Duplicate rows (%)< 0.1%
Total size in memory75.8 MiB
Average record size in memory504.0 B

Variable types

Text3
Unsupported16
Categorical28
Numeric16

Alerts

icmp.unused has constant value ""Constant
http.tls_port has constant value ""Constant
dns.qry.type has constant value ""Constant
dns.retransmit_request_in has constant value ""Constant
mqtt.msg_decoded_as has constant value ""Constant
mbtcp.len has constant value ""Constant
mbtcp.trans_id has constant value ""Constant
mbtcp.unit_id has constant value ""Constant
Dataset has 43 (< 0.1%) duplicate rowsDuplicates
icmp.checksum is highly overall correlated with icmp.seq_leHigh correlation
icmp.seq_le is highly overall correlated with icmp.checksumHigh correlation
tcp.ack is highly overall correlated with tcp.ack_raw and 2 other fieldsHigh correlation
tcp.ack_raw is highly overall correlated with tcp.ack and 4 other fieldsHigh correlation
tcp.checksum is highly overall correlated with tcp.dstport and 2 other fieldsHigh correlation
tcp.dstport is highly overall correlated with tcp.ack and 5 other fieldsHigh correlation
tcp.flags is highly overall correlated with tcp.ack_raw and 9 other fieldsHigh correlation
tcp.len is highly overall correlated with tcp.flagsHigh correlation
tcp.seq is highly overall correlated with tcp.ack_raw and 2 other fieldsHigh correlation
udp.port is highly overall correlated with udp.time_delta and 2 other fieldsHigh correlation
udp.time_delta is highly overall correlated with udp.port and 2 other fieldsHigh correlation
dns.qry.qu is highly overall correlated with udp.port and 1 other fieldsHigh correlation
arp.opcode is highly overall correlated with arp.hw.sizeHigh correlation
arp.hw.size is highly overall correlated with arp.opcodeHigh correlation
tcp.connection.rst is highly overall correlated with tcp.dstport and 1 other fieldsHigh correlation
tcp.connection.syn is highly overall correlated with tcp.ack and 2 other fieldsHigh correlation
tcp.connection.synack is highly overall correlated with tcp.flagsHigh correlation
tcp.flags.ack is highly overall correlated with tcp.ack_raw and 4 other fieldsHigh correlation
dns.retransmission is highly overall correlated with udp.port and 1 other fieldsHigh correlation
mqtt.conflag.cleansess is highly overall correlated with mqtt.conflags and 5 other fieldsHigh correlation
mqtt.conflags is highly overall correlated with mqtt.conflag.cleansess and 5 other fieldsHigh correlation
mqtt.hdrflags is highly overall correlated with mqtt.conflag.cleansess and 6 other fieldsHigh correlation
mqtt.len is highly overall correlated with mqtt.conflag.cleansess and 6 other fieldsHigh correlation
mqtt.msgtype is highly overall correlated with mqtt.conflag.cleansess and 6 other fieldsHigh correlation
mqtt.proto_len is highly overall correlated with mqtt.conflag.cleansess and 5 other fieldsHigh correlation
mqtt.topic_len is highly overall correlated with mqtt.hdrflags and 2 other fieldsHigh correlation
mqtt.ver is highly overall correlated with mqtt.conflag.cleansess and 5 other fieldsHigh correlation
Attack_label is highly overall correlated with Attack_typeHigh correlation
Attack_type is highly overall correlated with tcp.flags and 2 other fieldsHigh correlation
arp.opcode is highly imbalanced (94.3%)Imbalance
arp.hw.size is highly imbalanced (91.9%)Imbalance
http.response is highly imbalanced (73.2%)Imbalance
tcp.connection.fin is highly imbalanced (68.0%)Imbalance
tcp.connection.rst is highly imbalanced (55.0%)Imbalance
tcp.connection.synack is highly imbalanced (80.6%)Imbalance
dns.retransmission is highly imbalanced (99.9%)Imbalance
dns.retransmit_request is highly imbalanced (> 99.9%)Imbalance
mqtt.conflag.cleansess is highly imbalanced (93.3%)Imbalance
mqtt.conflags is highly imbalanced (93.3%)Imbalance
mqtt.hdrflags is highly imbalanced (88.4%)Imbalance
mqtt.len is highly imbalanced (89.9%)Imbalance
mqtt.msgtype is highly imbalanced (88.4%)Imbalance
mqtt.proto_len is highly imbalanced (93.3%)Imbalance
mqtt.topic_len is highly imbalanced (93.4%)Imbalance
mqtt.ver is highly imbalanced (93.3%)Imbalance
icmp.transmit_timestamp is highly skewed (γ1 = 43.56938013)Skewed
http.content_length is highly skewed (γ1 = 306.8700196)Skewed
tcp.len is highly skewed (γ1 = 41.85241122)Skewed
udp.port is highly skewed (γ1 = 81.93953209)Skewed
udp.time_delta is highly skewed (γ1 = 31.65620669)Skewed
dns.qry.qu is highly skewed (γ1 = 33.36643661)Skewed
arp.dst.proto_ipv4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
arp.src.proto_ipv4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
http.file_data is an unsupported type, check if it needs cleaning or further analysisUnsupported
http.request.uri.query is an unsupported type, check if it needs cleaning or further analysisUnsupported
http.request.method is an unsupported type, check if it needs cleaning or further analysisUnsupported
http.referer is an unsupported type, check if it needs cleaning or further analysisUnsupported
http.request.full_uri is an unsupported type, check if it needs cleaning or further analysisUnsupported
http.request.version is an unsupported type, check if it needs cleaning or further analysisUnsupported
tcp.options is an unsupported type, check if it needs cleaning or further analysisUnsupported
tcp.payload is an unsupported type, check if it needs cleaning or further analysisUnsupported
tcp.srcport is an unsupported type, check if it needs cleaning or further analysisUnsupported
dns.qry.name.len is an unsupported type, check if it needs cleaning or further analysisUnsupported
mqtt.conack.flags is an unsupported type, check if it needs cleaning or further analysisUnsupported
mqtt.msg is an unsupported type, check if it needs cleaning or further analysisUnsupported
mqtt.protoname is an unsupported type, check if it needs cleaning or further analysisUnsupported
mqtt.topic is an unsupported type, check if it needs cleaning or further analysisUnsupported
icmp.checksum has 143130 (90.7%) zerosZeros
icmp.seq_le has 142143 (90.1%) zerosZeros
icmp.transmit_timestamp has 157717 (99.9%) zerosZeros
http.content_length has 148340 (94.0%) zerosZeros
tcp.ack has 47155 (29.9%) zerosZeros
tcp.ack_raw has 47155 (29.9%) zerosZeros
tcp.checksum has 33920 (21.5%) zerosZeros
tcp.dstport has 33920 (21.5%) zerosZeros
tcp.flags has 33920 (21.5%) zerosZeros
tcp.len has 115494 (73.2%) zerosZeros
tcp.seq has 58508 (37.1%) zerosZeros
udp.port has 157395 (99.7%) zerosZeros
udp.stream has 143264 (90.8%) zerosZeros
udp.time_delta has 157395 (99.7%) zerosZeros
dns.qry.name has 156144 (99.0%) zerosZeros
dns.qry.qu has 157544 (99.8%) zerosZeros

Reproduction

Analysis started2023-10-09 16:33:36.906910
Analysis finished2023-10-09 16:36:06.800395
Duration2 minutes and 29.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct155186
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:07.370669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length25
Mean length23.775539
Min length3

Characters and Unicode

Total characters3751780
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155182 ?
Unique (%)98.3%

Sample

1st row6.0
2nd row6.0
3rd row6.0
4th row6.0
5th row6.0
ValueCountFrequency (%)
2021 142088
47.4%
192.168.0.128 1402
 
0.5%
0.0 873
 
0.3%
6.0 341
 
0.1%
23:12:11.894359000 2
 
< 0.1%
22:14:31.910544000 1
 
< 0.1%
22:15:14.613957000 1
 
< 0.1%
22:14:55.311461000 1
 
< 0.1%
22:14:30.939803000 1
 
< 0.1%
22:14:30.939896000 1
 
< 0.1%
Other values (155177) 155177
51.7%
2023-10-09T21:36:08.205968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 759393
20.2%
2 538697
14.4%
1 452957
12.1%
426263
11.4%
: 284176
 
7.6%
3 210989
 
5.6%
. 186796
 
5.0%
4 166857
 
4.4%
5 161185
 
4.3%
9 158829
 
4.2%
Other values (3) 405638
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2854545
76.1%
Other Punctuation 470972
 
12.6%
Space Separator 426263
 
11.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 759393
26.6%
2 538697
18.9%
1 452957
15.9%
3 210989
 
7.4%
4 166857
 
5.8%
5 161185
 
5.6%
9 158829
 
5.6%
8 142367
 
5.0%
6 133184
 
4.7%
7 130087
 
4.6%
Other Punctuation
ValueCountFrequency (%)
: 284176
60.3%
. 186796
39.7%
Space Separator
ValueCountFrequency (%)
426263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3751780
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 759393
20.2%
2 538697
14.4%
1 452957
12.1%
426263
11.4%
: 284176
 
7.6%
3 210989
 
5.6%
. 186796
 
5.0%
4 166857
 
4.4%
5 161185
 
4.3%
9 158829
 
4.2%
Other values (3) 405638
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3751780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 759393
20.2%
2 538697
14.4%
1 452957
12.1%
426263
11.4%
: 284176
 
7.6%
3 210989
 
5.6%
. 186796
 
5.0%
4 166857
 
4.4%
5 161185
 
4.3%
9 158829
 
4.2%
Other values (3) 405638
10.8%
Distinct19090
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:08.437672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length12.425798
Min length1

Characters and Unicode

Total characters1960791
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19077 ?
Unique (%)12.1%

Sample

1st row192.168.0.152
2nd row192.168.0.101
3rd row192.168.0.152
4th row192.168.0.101
5th row192.168.0.152
ValueCountFrequency (%)
192.168.0.128 72546
46.0%
192.168.0.170 47688
30.2%
192.168.0.101 10126
 
6.4%
0 7991
 
5.1%
0.0.0.0 208
 
0.1%
192.168.0.152 120
 
0.1%
172.217.19.42 17
 
< 0.1%
192.168.0.1 9
 
< 0.1%
104.16.87.20 7
 
< 0.1%
142.250.200.205 5
 
< 0.1%
Other values (19080) 19083
 
12.1%
2023-10-09T21:36:08.895213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 449785
22.9%
. 449427
22.9%
2 240939
12.3%
8 214757
11.0%
0 210923
10.8%
6 143624
 
7.3%
9 143100
 
7.3%
7 61302
 
3.1%
4 16383
 
0.8%
3 16200
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1511364
77.1%
Other Punctuation 449427
 
22.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 449785
29.8%
2 240939
15.9%
8 214757
14.2%
0 210923
14.0%
6 143624
 
9.5%
9 143100
 
9.5%
7 61302
 
4.1%
4 16383
 
1.1%
3 16200
 
1.1%
5 14351
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 449427
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1960791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 449785
22.9%
. 449427
22.9%
2 240939
12.3%
8 214757
11.0%
0 210923
10.8%
6 143624
 
7.3%
9 143100
 
7.3%
7 61302
 
3.1%
4 16383
 
0.8%
3 16200
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1960791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 449785
22.9%
. 449427
22.9%
2 240939
12.3%
8 214757
11.0%
0 210923
10.8%
6 143624
 
7.3%
9 143100
 
7.3%
7 61302
 
3.1%
4 16383
 
0.8%
3 16200
 
0.8%
Distinct8084
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:09.097952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length11.39699
Min length1

Characters and Unicode

Total characters1798445
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8070 ?
Unique (%)5.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
192.168.0.128 75373
47.8%
192.168.0.170 42290
26.8%
0 20214
 
12.8%
192.168.0.101 10097
 
6.4%
0.0 1214
 
0.8%
224.0.0.251 189
 
0.1%
224.0.0.1 150
 
0.1%
224.0.0.252 138
 
0.1%
192.168.0.1 21
 
< 0.1%
172.217.19.42 19
 
< 0.1%
Other values (8074) 8095
 
5.1%
2023-10-09T21:36:09.923472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 414211
23.0%
. 410330
22.8%
2 220074
12.2%
0 209642
11.7%
8 208019
11.6%
6 133309
 
7.4%
9 133221
 
7.4%
7 47921
 
2.7%
4 8663
 
0.5%
3 6592
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1388115
77.2%
Other Punctuation 410330
 
22.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 414211
29.8%
2 220074
15.9%
0 209642
15.1%
8 208019
15.0%
6 133309
 
9.6%
9 133221
 
9.6%
7 47921
 
3.5%
4 8663
 
0.6%
3 6592
 
0.5%
5 6463
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 410330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1798445
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 414211
23.0%
. 410330
22.8%
2 220074
12.2%
0 209642
11.7%
8 208019
11.6%
6 133309
 
7.4%
9 133221
 
7.4%
7 47921
 
2.7%
4 8663
 
0.5%
3 6592
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1798445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 414211
23.0%
. 410330
22.8%
2 220074
12.2%
0 209642
11.7%
8 208019
11.6%
6 133309
 
7.4%
9 133221
 
7.4%
7 47921
 
2.7%
4 8663
 
0.5%
3 6592
 
0.4%

arp.dst.proto_ipv4
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

arp.opcode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156226 
1.0
 
908
2.0
 
666

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156226
99.0%
1.0 908
 
0.6%
2.0 666
 
0.4%

Length

2023-10-09T21:36:10.156158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:10.472717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156226
99.0%
1.0 908
 
0.6%
2.0 666
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 314026
66.3%
. 157800
33.3%
1 908
 
0.2%
2 666
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314026
99.5%
1 908
 
0.3%
2 666
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314026
66.3%
. 157800
33.3%
1 908
 
0.2%
2 666
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314026
66.3%
. 157800
33.3%
1 908
 
0.2%
2 666
 
0.1%

arp.hw.size
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156226 
6.0
 
1574

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156226
99.0%
6.0 1574
 
1.0%

Length

2023-10-09T21:36:10.803515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:11.020735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156226
99.0%
6.0 1574
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 314026
66.3%
. 157800
33.3%
6 1574
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314026
99.5%
6 1574
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314026
66.3%
. 157800
33.3%
6 1574
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314026
66.3%
. 157800
33.3%
6 1574
 
0.3%

arp.src.proto_ipv4
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

icmp.checksum
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13187
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3047.2918
Minimum0
Maximum65532
Zeros143130
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:11.238492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30082.2
Maximum65532
Range65532
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11144.328
Coefficient of variation (CV)3.6571253
Kurtosis14.72532
Mean3047.2918
Median Absolute Deviation (MAD)0
Skewness3.9160067
Sum4.8086265 × 108
Variance1.2419605 × 108
MonotonicityNot monotonic
2023-10-09T21:36:11.472374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143130
90.7%
63737 4
 
< 0.1%
8993 4
 
< 0.1%
49043 4
 
< 0.1%
56580 4
 
< 0.1%
35144 4
 
< 0.1%
27936 4
 
< 0.1%
41268 4
 
< 0.1%
45186 3
 
< 0.1%
17045 3
 
< 0.1%
Other values (13177) 14636
 
9.3%
ValueCountFrequency (%)
0 143130
90.7%
4 1
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
24 1
 
< 0.1%
28 1
 
< 0.1%
38 1
 
< 0.1%
42 2
 
< 0.1%
44 2
 
< 0.1%
45 1
 
< 0.1%
ValueCountFrequency (%)
65532 1
< 0.1%
65531 1
< 0.1%
65530 1
< 0.1%
65522 1
< 0.1%
65520 1
< 0.1%
65519 1
< 0.1%
65513 1
< 0.1%
65512 2
< 0.1%
65509 1
< 0.1%
65507 1
< 0.1%

icmp.seq_le
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13824
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3239.9798
Minimum0
Maximum65524
Zeros142143
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:11.631598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile32598.1
Maximum65524
Range65524
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11406.073
Coefficient of variation (CV)3.5204149
Kurtosis13.296843
Mean3239.9798
Median Absolute Deviation (MAD)0
Skewness3.7412448
Sum5.1126881 × 108
Variance1.300985 × 108
MonotonicityNot monotonic
2023-10-09T21:36:11.789513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142143
90.1%
256 166
 
0.1%
65207 4
 
< 0.1%
32384 4
 
< 0.1%
34999 4
 
< 0.1%
45095 4
 
< 0.1%
62419 4
 
< 0.1%
14422 4
 
< 0.1%
26422 4
 
< 0.1%
55363 4
 
< 0.1%
Other values (13814) 15459
 
9.8%
ValueCountFrequency (%)
0 142143
90.1%
1 1
 
< 0.1%
17 1
 
< 0.1%
19 1
 
< 0.1%
25 1
 
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
39 1
 
< 0.1%
47 1
 
< 0.1%
56 1
 
< 0.1%
ValueCountFrequency (%)
65524 1
< 0.1%
65517 1
< 0.1%
65516 1
< 0.1%
65513 1
< 0.1%
65497 1
< 0.1%
65491 1
< 0.1%
65488 1
< 0.1%
65487 1
< 0.1%
65466 1
< 0.1%
65461 1
< 0.1%

icmp.transmit_timestamp
Real number (ℝ)

SKEWED  ZEROS 

Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40468.161
Minimum0
Maximum77289023
Zeros157717
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:11.967575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77289023
Range77289023
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1764074.5
Coefficient of variation (CV)43.591666
Kurtosis1896.3314
Mean40468.161
Median Absolute Deviation (MAD)0
Skewness43.56938
Sum6.3858757 × 109
Variance3.111959 × 1012
MonotonicityNot monotonic
2023-10-09T21:36:12.155845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157717
99.9%
77041589 1
 
< 0.1%
77125159 1
 
< 0.1%
77115472 1
 
< 0.1%
77096697 1
 
< 0.1%
77078849 1
 
< 0.1%
77070441 1
 
< 0.1%
77063273 1
 
< 0.1%
77056580 1
 
< 0.1%
77050926 1
 
< 0.1%
Other values (74) 74
 
< 0.1%
ValueCountFrequency (%)
0 157717
99.9%
76471910 1
 
< 0.1%
76496315 1
 
< 0.1%
76514613 1
 
< 0.1%
76531277 1
 
< 0.1%
76549573 1
 
< 0.1%
76557832 1
 
< 0.1%
76568891 1
 
< 0.1%
76588882 1
 
< 0.1%
76595757 1
 
< 0.1%
ValueCountFrequency (%)
77289023 1
< 0.1%
77284157 1
< 0.1%
77274626 1
< 0.1%
77268553 1
< 0.1%
77264171 1
< 0.1%
77257774 1
< 0.1%
77250122 1
< 0.1%
77245077 1
< 0.1%
77230327 1
< 0.1%
77220906 1
< 0.1%

icmp.unused
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:12.343272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:12.462265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

http.file_data
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

http.content_length
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.71552
Minimum0
Maximum83655
Zeros148340
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:12.666022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile36
Maximum83655
Range83655
Interquartile range (IQR)0

Descriptive statistics

Standard deviation229.65967
Coefficient of variation (CV)15.60663
Kurtosis111491.25
Mean14.71552
Median Absolute Deviation (MAD)0
Skewness306.87002
Sum2322109
Variance52743.565
MonotonicityNot monotonic
2023-10-09T21:36:12.846994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 148340
94.0%
277 4487
 
2.8%
57 1779
 
1.1%
36 1240
 
0.8%
303 343
 
0.2%
1465 232
 
0.1%
5 229
 
0.1%
1415 227
 
0.1%
12 163
 
0.1%
38 125
 
0.1%
Other values (23) 635
 
0.4%
ValueCountFrequency (%)
0 148340
94.0%
1 61
 
< 0.1%
5 229
 
0.1%
6 5
 
< 0.1%
12 163
 
0.1%
22 41
 
< 0.1%
36 1240
 
0.8%
37 117
 
0.1%
38 125
 
0.1%
39 22
 
< 0.1%
ValueCountFrequency (%)
83655 1
 
< 0.1%
1465 232
0.1%
1415 227
0.1%
1404 1
 
< 0.1%
1155 1
 
< 0.1%
714 12
 
< 0.1%
315 2
 
< 0.1%
307 5
 
< 0.1%
303 343
0.2%
301 1
 
< 0.1%

http.request.uri.query
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

http.request.method
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

http.referer
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

http.request.full_uri
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

http.request.version
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

http.response
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
150581 
1.0
 
7219

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 150581
95.4%
1.0 7219
 
4.6%

Length

2023-10-09T21:36:13.018092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:13.151086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 150581
95.4%
1.0 7219
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 308381
65.1%
. 157800
33.3%
1 7219
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 308381
97.7%
1 7219
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 308381
65.1%
. 157800
33.3%
1 7219
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 308381
65.1%
. 157800
33.3%
1 7219
 
1.5%

http.tls_port
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:13.272006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:13.383089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

tcp.ack
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27929
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71600387
Minimum0
Maximum2.1473333 × 109
Zeros47155
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:13.522049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q3479
95-th percentile5.1484458 × 108
Maximum2.1473333 × 109
Range2.1473333 × 109
Interquartile range (IQR)479

Descriptive statistics

Standard deviation3.1012306 × 108
Coefficient of variation (CV)4.3313043
Kurtosis22.502304
Mean71600387
Median Absolute Deviation (MAD)1
Skewness4.7554839
Sum1.1298541 × 1013
Variance9.6176315 × 1016
MonotonicityNot monotonic
2023-10-09T21:36:13.748979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47155
29.9%
1 33569
21.3%
121 4631
 
2.9%
6 3707
 
2.3%
5 2589
 
1.6%
15 2589
 
1.6%
59 2528
 
1.6%
479 1543
 
1.0%
108 1528
 
1.0%
103 1438
 
0.9%
Other values (27919) 56523
35.8%
ValueCountFrequency (%)
0 47155
29.9%
1 33569
21.3%
2 508
 
0.3%
5 2589
 
1.6%
6 3707
 
2.3%
9 1
 
< 0.1%
13 4
 
< 0.1%
14 1
 
< 0.1%
15 2589
 
1.6%
16 1
 
< 0.1%
ValueCountFrequency (%)
2147333291 1
< 0.1%
2147250934 1
< 0.1%
2147087720 1
< 0.1%
2147021222 1
< 0.1%
2146756557 1
< 0.1%
2146640031 1
< 0.1%
2146540233 1
< 0.1%
2146465351 1
< 0.1%
2146331860 1
< 0.1%
2145863033 1
< 0.1%

tcp.ack_raw
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94716
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3583475 × 109
Minimum0
Maximum4.2949472 × 109
Zeros47155
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:14.041265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1600508 × 109
Q32.3722277 × 109
95-th percentile3.8168376 × 109
Maximum4.2949472 × 109
Range4.2949472 × 109
Interquartile range (IQR)2.3722277 × 109

Descriptive statistics

Standard deviation1.295523 × 109
Coefficient of variation (CV)0.95374931
Kurtosis-0.93513254
Mean1.3583475 × 109
Median Absolute Deviation (MAD)1.1600508 × 109
Skewness0.52610897
Sum2.1434723 × 1014
Variance1.6783797 × 1018
MonotonicityNot monotonic
2023-10-09T21:36:14.302257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47155
29.9%
2013966124 3002
 
1.9%
2213215181 2544
 
1.6%
1453306566 2082
 
1.3%
1013594299 1304
 
0.8%
1679853537 15
 
< 0.1%
3322479508 14
 
< 0.1%
2208222993 13
 
< 0.1%
3004336843 13
 
< 0.1%
3903830764 13
 
< 0.1%
Other values (94706) 101645
64.4%
ValueCountFrequency (%)
0 47155
29.9%
168003 1
 
< 0.1%
199587 1
 
< 0.1%
207718 1
 
< 0.1%
277631 1
 
< 0.1%
277632 1
 
< 0.1%
318739 1
 
< 0.1%
335609 1
 
< 0.1%
346568 1
 
< 0.1%
347765 1
 
< 0.1%
ValueCountFrequency (%)
4294947151 1
 
< 0.1%
4294731662 1
 
< 0.1%
4294731175 1
 
< 0.1%
4294727148 1
 
< 0.1%
4294725888 1
 
< 0.1%
4294725196 1
 
< 0.1%
4294700661 1
 
< 0.1%
4294632609 1
 
< 0.1%
4294629165 3
< 0.1%
4294564634 1
 
< 0.1%

tcp.checksum
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55513
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25796.599
Minimum0
Maximum65535
Zeros33920
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:14.529022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12982
median23906
Q344733
95-th percentile61381.05
Maximum65535
Range65535
Interquartile range (IQR)41751

Descriptive statistics

Standard deviation21513.031
Coefficient of variation (CV)0.83394834
Kurtosis-1.3127537
Mean25796.599
Median Absolute Deviation (MAD)20872
Skewness0.25281798
Sum4.0707033 × 109
Variance4.6281051 × 108
MonotonicityNot monotonic
2023-10-09T21:36:14.748134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33920
 
21.5%
51526 12
 
< 0.1%
64783 12
 
< 0.1%
61198 10
 
< 0.1%
22207 10
 
< 0.1%
53888 10
 
< 0.1%
33678 10
 
< 0.1%
52685 9
 
< 0.1%
32974 9
 
< 0.1%
10315 9
 
< 0.1%
Other values (55503) 123789
78.4%
ValueCountFrequency (%)
0 33920
21.5%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
8 5
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
65535 1
 
< 0.1%
65534 5
< 0.1%
65533 3
< 0.1%
65531 1
 
< 0.1%
65530 2
 
< 0.1%
65529 2
 
< 0.1%
65528 1
 
< 0.1%
65527 2
 
< 0.1%
65526 2
 
< 0.1%
65525 2
 
< 0.1%

tcp.connection.fin
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
148625 
1.0
 
9175

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 148625
94.2%
1.0 9175
 
5.8%

Length

2023-10-09T21:36:14.913226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:15.035216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 148625
94.2%
1.0 9175
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 306425
64.7%
. 157800
33.3%
1 9175
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 306425
97.1%
1 9175
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 306425
64.7%
. 157800
33.3%
1 9175
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 306425
64.7%
. 157800
33.3%
1 9175
 
1.9%

tcp.connection.rst
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
142948 
1.0
14852 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 142948
90.6%
1.0 14852
 
9.4%

Length

2023-10-09T21:36:15.164328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:15.284471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 142948
90.6%
1.0 14852
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 300748
63.5%
. 157800
33.3%
1 14852
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 300748
95.3%
1 14852
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 300748
63.5%
. 157800
33.3%
1 14852
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 300748
63.5%
. 157800
33.3%
1 14852
 
3.1%

tcp.connection.syn
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
137625 
1.0
20175 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 137625
87.2%
1.0 20175
 
12.8%

Length

2023-10-09T21:36:15.414484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:15.554081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 137625
87.2%
1.0 20175
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0 295425
62.4%
. 157800
33.3%
1 20175
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 295425
93.6%
1 20175
 
6.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 295425
62.4%
. 157800
33.3%
1 20175
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 295425
62.4%
. 157800
33.3%
1 20175
 
4.3%

tcp.connection.synack
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
153074 
1.0
 
4726

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 153074
97.0%
1.0 4726
 
3.0%

Length

2023-10-09T21:36:15.685992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:15.801284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 153074
97.0%
1.0 4726
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 310874
65.7%
. 157800
33.3%
1 4726
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 310874
98.5%
1 4726
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 310874
65.7%
. 157800
33.3%
1 4726
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 310874
65.7%
. 157800
33.3%
1 4726
 
1.0%

tcp.dstport
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23188
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17964.646
Minimum0
Maximum65535
Zeros33920
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:15.933285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180
median1883
Q345494
95-th percentile60320.1
Maximum65535
Range65535
Interquartile range (IQR)45414

Descriptive statistics

Standard deviation24154.223
Coefficient of variation (CV)1.3445421
Kurtosis-1.1793578
Mean17964.646
Median Absolute Deviation (MAD)1883
Skewness0.80421922
Sum2.8348211 × 109
Variance5.8342649 × 108
MonotonicityNot monotonic
2023-10-09T21:36:16.096205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 41581
26.4%
0 33920
21.5%
1883 9951
 
6.3%
4321 9890
 
6.3%
47924 2961
 
1.9%
60944 2774
 
1.8%
60210 2502
 
1.6%
56322 1976
 
1.3%
3333 1819
 
1.2%
35306 1263
 
0.8%
Other values (23178) 49163
31.2%
ValueCountFrequency (%)
0 33920
21.5%
35 1
 
< 0.1%
52 1
 
< 0.1%
80 41581
26.4%
123 1
 
< 0.1%
138 1
 
< 0.1%
140 1
 
< 0.1%
161 1
 
< 0.1%
236 1
 
< 0.1%
242 1
 
< 0.1%
ValueCountFrequency (%)
65535 135
0.1%
65533 3
 
< 0.1%
65528 2
 
< 0.1%
65526 1
 
< 0.1%
65525 1
 
< 0.1%
65523 2
 
< 0.1%
65520 1
 
< 0.1%
65518 1
 
< 0.1%
65517 1
 
< 0.1%
65515 2
 
< 0.1%

tcp.flags
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.613999
Minimum0
Maximum25
Zeros33920
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:16.228198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q320
95-th percentile24
Maximum25
Range25
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.3191355
Coefficient of variation (CV)0.73879312
Kurtosis-1.5274921
Mean12.613999
Median Absolute Deviation (MAD)8
Skewness-0.28755333
Sum1990489
Variance86.846287
MonotonicityNot monotonic
2023-10-09T21:36:16.352216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
16 42722
27.1%
0 33920
21.5%
24 32230
20.4%
2 20175
12.8%
20 11389
 
7.2%
17 7876
 
5.0%
18 4726
 
3.0%
4 3463
 
2.2%
25 1299
 
0.8%
ValueCountFrequency (%)
0 33920
21.5%
2 20175
12.8%
4 3463
 
2.2%
16 42722
27.1%
17 7876
 
5.0%
18 4726
 
3.0%
20 11389
 
7.2%
24 32230
20.4%
25 1299
 
0.8%
ValueCountFrequency (%)
25 1299
 
0.8%
24 32230
20.4%
20 11389
 
7.2%
18 4726
 
3.0%
17 7876
 
5.0%
16 42722
27.1%
4 3463
 
2.2%
2 20175
12.8%
0 33920
21.5%

tcp.flags.ack
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1.0
100242 
0.0
57558 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 100242
63.5%
0.0 57558
36.5%

Length

2023-10-09T21:36:16.480500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:16.598945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 100242
63.5%
0.0 57558
36.5%

Most occurring characters

ValueCountFrequency (%)
0 215358
45.5%
. 157800
33.3%
1 100242
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 215358
68.2%
1 100242
31.8%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 215358
45.5%
. 157800
33.3%
1 100242
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 215358
45.5%
. 157800
33.3%
1 100242
21.2%

tcp.len
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct786
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.77925
Minimum0
Maximum65228
Zeros115494
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:16.734953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile495
Maximum65228
Range65228
Interquartile range (IQR)14

Descriptive statistics

Standard deviation1307.0376
Coefficient of variation (CV)10.071237
Kurtosis1870.7788
Mean129.77925
Median Absolute Deviation (MAD)0
Skewness41.852411
Sum20479166
Variance1708347.3
MonotonicityNot monotonic
2023-10-09T21:36:16.888945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115494
73.2%
120 6214
 
3.9%
464 3810
 
2.4%
495 3670
 
2.3%
1440 2057
 
1.3%
4 1296
 
0.8%
41 1291
 
0.8%
14 1288
 
0.8%
2 1278
 
0.8%
304 1276
 
0.8%
Other values (776) 20126
 
12.8%
ValueCountFrequency (%)
0 115494
73.2%
2 1278
 
0.8%
4 1296
 
0.8%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 19
 
< 0.1%
9 20
 
< 0.1%
10 26
 
< 0.1%
11 35
 
< 0.1%
12 40
 
< 0.1%
ValueCountFrequency (%)
65228 2
< 0.1%
64745 2
< 0.1%
64719 2
< 0.1%
63766 2
< 0.1%
63288 2
< 0.1%
63237 2
< 0.1%
63027 2
< 0.1%
62937 2
< 0.1%
62897 1
< 0.1%
62377 2
< 0.1%

tcp.options
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

tcp.payload
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

tcp.seq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18199
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1875111.3
Minimum0
Maximum2.079647 × 108
Zeros58508
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:17.045358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q3119
95-th percentile648659.55
Maximum2.079647 × 108
Range2.079647 × 108
Interquartile range (IQR)119

Descriptive statistics

Standard deviation15797069
Coefficient of variation (CV)8.4246033
Kurtosis98.5967
Mean1875111.3
Median Absolute Deviation (MAD)1
Skewness9.6912638
Sum2.9589256 × 1011
Variance2.4954738 × 1014
MonotonicityNot monotonic
2023-10-09T21:36:17.343355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58508
37.1%
1 41956
26.6%
59 3764
 
2.4%
6 3629
 
2.3%
5 2580
 
1.6%
479 1428
 
0.9%
56 1280
 
0.8%
15 1259
 
0.8%
103 1243
 
0.8%
108 1094
 
0.7%
Other values (18189) 41059
26.0%
ValueCountFrequency (%)
0 58508
37.1%
4 × 10-51
 
< 0.1%
4.1 × 10-51
 
< 0.1%
8.3 × 10-51
 
< 0.1%
9.5 × 10-51
 
< 0.1%
9.6 × 10-51
 
< 0.1%
9.7 × 10-51
 
< 0.1%
0.000102 1
 
< 0.1%
0.000107 1
 
< 0.1%
0.000166 1
 
< 0.1%
ValueCountFrequency (%)
207964705 1
< 0.1%
207761537 1
< 0.1%
207704140 1
< 0.1%
207696664 1
< 0.1%
207513059 1
< 0.1%
207470216 1
< 0.1%
207394137 1
< 0.1%
207388377 1
< 0.1%
207380192 1
< 0.1%
207338463 1
< 0.1%

tcp.srcport
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

udp.port
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7484791
Minimum0
Maximum60310
Zeros157395
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:17.468280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum60310
Range60310
Interquartile range (IQR)0

Descriptive statistics

Standard deviation613.44483
Coefficient of variation (CV)79.169708
Kurtosis6839.4814
Mean7.7484791
Median Absolute Deviation (MAD)0
Skewness81.939532
Sum1222710
Variance376314.56
MonotonicityNot monotonic
2023-10-09T21:36:17.603586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 157395
99.7%
22 216
 
0.1%
21 77
 
< 0.1%
15 74
 
< 0.1%
53 7
 
< 0.1%
123 4
 
< 0.1%
5353 2
 
< 0.1%
41030 1
 
< 0.1%
39447 1
 
< 0.1%
48939 1
 
< 0.1%
Other values (22) 22
 
< 0.1%
ValueCountFrequency (%)
0 157395
99.7%
15 74
 
< 0.1%
21 77
 
< 0.1%
22 216
 
0.1%
53 7
 
< 0.1%
123 4
 
< 0.1%
5353 2
 
< 0.1%
32770 1
 
< 0.1%
33864 1
 
< 0.1%
37607 1
 
< 0.1%
ValueCountFrequency (%)
60310 1
< 0.1%
59638 1
< 0.1%
58714 1
< 0.1%
56388 1
< 0.1%
53818 1
< 0.1%
52570 1
< 0.1%
52059 1
< 0.1%
51853 1
< 0.1%
51491 1
< 0.1%
51181 1
< 0.1%

udp.stream
Real number (ℝ)

ZEROS 

Distinct14492
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121140.48
Minimum0
Maximum2898725
Zeros143264
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:17.760497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1156645.9
Maximum2898725
Range2898725
Interquartile range (IQR)0

Descriptive statistics

Standard deviation468760.69
Coefficient of variation (CV)3.8695627
Kurtosis16.852035
Mean121140.48
Median Absolute Deviation (MAD)0
Skewness4.1667027
Sum1.9115968 × 1010
Variance2.1973658 × 1011
MonotonicityNot monotonic
2023-10-09T21:36:17.943497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143264
90.8%
53 21
 
< 0.1%
123 3
 
< 0.1%
20824 2
 
< 0.1%
49350 2
 
< 0.1%
13438 2
 
< 0.1%
21507 2
 
< 0.1%
511 2
 
< 0.1%
22302 2
 
< 0.1%
1220 2
 
< 0.1%
Other values (14482) 14498
 
9.2%
ValueCountFrequency (%)
0 143264
90.8%
12 1
 
< 0.1%
53 21
 
< 0.1%
107 1
 
< 0.1%
123 3
 
< 0.1%
164 1
 
< 0.1%
222 1
 
< 0.1%
226 1
 
< 0.1%
300 1
 
< 0.1%
334 1
 
< 0.1%
ValueCountFrequency (%)
2898725 1
< 0.1%
2897835 1
< 0.1%
2897710 1
< 0.1%
2897449 1
< 0.1%
2897438 1
< 0.1%
2897305 1
< 0.1%
2897216 1
< 0.1%
2896875 1
< 0.1%
2896822 1
< 0.1%
2896547 1
< 0.1%

udp.time_delta
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34140684
Minimum0
Maximum507
Zeros157395
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:18.134677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum507
Range507
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.6861918
Coefficient of variation (CV)28.371405
Kurtosis1083.1245
Mean0.34140684
Median Absolute Deviation (MAD)0
Skewness31.656207
Sum53874
Variance93.822312
MonotonicityNot monotonic
2023-10-09T21:36:18.334834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 157395
99.7%
12 216
 
0.1%
255 151
 
0.1%
303 3
 
< 0.1%
436 1
 
< 0.1%
398 1
 
< 0.1%
411 1
 
< 0.1%
419 1
 
< 0.1%
420 1
 
< 0.1%
430 1
 
< 0.1%
Other values (29) 29
 
< 0.1%
ValueCountFrequency (%)
0 157395
99.7%
7 1
 
< 0.1%
12 216
 
0.1%
18 1
 
< 0.1%
101 1
 
< 0.1%
107 1
 
< 0.1%
149 1
 
< 0.1%
179 1
 
< 0.1%
200 1
 
< 0.1%
204 1
 
< 0.1%
ValueCountFrequency (%)
507 1
< 0.1%
503 1
< 0.1%
502 1
< 0.1%
493 1
< 0.1%
487 1
< 0.1%
461 1
< 0.1%
451 1
< 0.1%
448 1
< 0.1%
446 1
< 0.1%
436 1
< 0.1%

dns.qry.name
Real number (ℝ)

ZEROS 

Distinct1430
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12700.612
Minimum0
Maximum2896968
Zeros156144
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:18.665772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2896968
Range2896968
Interquartile range (IQR)0

Descriptive statistics

Standard deviation156847.84
Coefficient of variation (CV)12.349628
Kurtosis210.52729
Mean12700.612
Median Absolute Deviation (MAD)0
Skewness14.086297
Sum2.0041565 × 109
Variance2.4601244 × 1010
MonotonicityNot monotonic
2023-10-09T21:36:18.981597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 156144
99.0%
1 227
 
0.1%
0.000139 2
 
< 0.1%
2896821 1
 
< 0.1%
2085891 1
 
< 0.1%
2103321 1
 
< 0.1%
2102485 1
 
< 0.1%
2097418 1
 
< 0.1%
2096631 1
 
< 0.1%
2095007 1
 
< 0.1%
Other values (1420) 1420
 
0.9%
ValueCountFrequency (%)
0 156144
99.0%
3.2 × 10-51
 
< 0.1%
4.1 × 10-51
 
< 0.1%
5.4 × 10-51
 
< 0.1%
5.7 × 10-51
 
< 0.1%
8.2 × 10-51
 
< 0.1%
0.000105 1
 
< 0.1%
0.000121 1
 
< 0.1%
0.000136 1
 
< 0.1%
0.000139 2
 
< 0.1%
ValueCountFrequency (%)
2896968 1
< 0.1%
2896821 1
< 0.1%
2896549 1
< 0.1%
2894872 1
< 0.1%
2894680 1
< 0.1%
2893594 1
< 0.1%
2890148 1
< 0.1%
2890090 1
< 0.1%
2889752 1
< 0.1%
2889134 1
< 0.1%

dns.qry.name.len
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

dns.qry.qu
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7786692
Minimum0
Maximum1028
Zeros157544
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-10-09T21:36:19.230114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1028
Range1028
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.063411
Coefficient of variation (CV)29.61901
Kurtosis1187.5601
Mean0.7786692
Median Absolute Deviation (MAD)0
Skewness33.366437
Sum122874
Variance531.92094
MonotonicityNot monotonic
2023-10-09T21:36:19.462180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157544
99.8%
21 28
 
< 0.1%
197 7
 
< 0.1%
477 7
 
< 0.1%
687 7
 
< 0.1%
688 7
 
< 0.1%
198 7
 
< 0.1%
476 7
 
< 0.1%
908 5
 
< 0.1%
829 5
 
< 0.1%
Other values (56) 176
 
0.1%
ValueCountFrequency (%)
0 157544
99.8%
21 28
 
< 0.1%
22 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
70 5
 
< 0.1%
71 5
 
< 0.1%
95 1
 
< 0.1%
96 1
 
< 0.1%
120 5
 
< 0.1%
ValueCountFrequency (%)
1028 5
< 0.1%
1027 5
< 0.1%
1001 1
 
< 0.1%
957 5
< 0.1%
954 5
< 0.1%
928 1
 
< 0.1%
925 1
 
< 0.1%
909 5
< 0.1%
908 5
< 0.1%
851 3
< 0.1%

dns.qry.type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:19.686035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:19.834619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

dns.retransmission
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157771 
1.0
 
21
28.0
 
7
12.0
 
1

Length

Max length4
Median length3
Mean length3.0000507
Min length3

Characters and Unicode

Total characters473408
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157771
> 99.9%
1.0 21
 
< 0.1%
28.0 7
 
< 0.1%
12.0 1
 
< 0.1%

Length

2023-10-09T21:36:19.996607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:20.176853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157771
> 99.9%
1.0 21
 
< 0.1%
28.0 7
 
< 0.1%
12.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 315571
66.7%
. 157800
33.3%
1 22
 
< 0.1%
2 8
 
< 0.1%
8 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315608
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315571
> 99.9%
1 22
 
< 0.1%
2 8
 
< 0.1%
8 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315571
66.7%
. 157800
33.3%
1 22
 
< 0.1%
2 8
 
< 0.1%
8 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315571
66.7%
. 157800
33.3%
1 22
 
< 0.1%
2 8
 
< 0.1%
8 7
 
< 0.1%

dns.retransmit_request
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157799 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157799
> 99.9%
1.0 1
 
< 0.1%

Length

2023-10-09T21:36:20.389109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:20.571050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157799
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 315599
66.7%
. 157800
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315599
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315599
66.7%
. 157800
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315599
66.7%
. 157800
33.3%
1 1
 
< 0.1%

dns.retransmit_request_in
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:20.745345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:20.888697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

mqtt.conack.flags
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

mqtt.conflag.cleansess
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156550 
1.0
 
1250

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156550
99.2%
1.0 1250
 
0.8%

Length

2023-10-09T21:36:21.033163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:21.164230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156550
99.2%
1.0 1250
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
1 1250
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314350
99.6%
1 1250
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
1 1250
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
1 1250
 
0.3%

mqtt.conflags
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156550 
2.0
 
1250

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156550
99.2%
2.0 1250
 
0.8%

Length

2023-10-09T21:36:21.320686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:21.483684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156550
99.2%
2.0 1250
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
2 1250
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314350
99.6%
2 1250
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
2 1250
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
2 1250
 
0.3%

mqtt.hdrflags
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
152737 
32.0
 
1289
224.0
 
1278
16.0
 
1250
48.0
 
1246

Length

Max length5
Median length3
Mean length3.0401838
Min length3

Characters and Unicode

Total characters479741
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 152737
96.8%
32.0 1289
 
0.8%
224.0 1278
 
0.8%
16.0 1250
 
0.8%
48.0 1246
 
0.8%

Length

2023-10-09T21:36:21.707928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:21.908454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 152737
96.8%
32.0 1289
 
0.8%
224.0 1278
 
0.8%
16.0 1250
 
0.8%
48.0 1246
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 310537
64.7%
. 157800
32.9%
2 3845
 
0.8%
4 2524
 
0.5%
3 1289
 
0.3%
1 1250
 
0.3%
6 1250
 
0.3%
8 1246
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321941
67.1%
Other Punctuation 157800
32.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 310537
96.5%
2 3845
 
1.2%
4 2524
 
0.8%
3 1289
 
0.4%
1 1250
 
0.4%
6 1250
 
0.4%
8 1246
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 479741
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 310537
64.7%
. 157800
32.9%
2 3845
 
0.8%
4 2524
 
0.5%
3 1289
 
0.3%
1 1250
 
0.3%
6 1250
 
0.3%
8 1246
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 479741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 310537
64.7%
. 157800
32.9%
2 3845
 
0.8%
4 2524
 
0.5%
3 1289
 
0.3%
1 1250
 
0.3%
6 1250
 
0.3%
8 1246
 
0.3%

mqtt.len
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
154015 
2.0
 
1289
12.0
 
1250
39.0
 
1246

Length

Max length4
Median length3
Mean length3.0158175
Min length3

Characters and Unicode

Total characters475896
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 154015
97.6%
2.0 1289
 
0.8%
12.0 1250
 
0.8%
39.0 1246
 
0.8%

Length

2023-10-09T21:36:22.123585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:22.289271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 154015
97.6%
2.0 1289
 
0.8%
12.0 1250
 
0.8%
39.0 1246
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 311815
65.5%
. 157800
33.2%
2 2539
 
0.5%
1 1250
 
0.3%
3 1246
 
0.3%
9 1246
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 318096
66.8%
Other Punctuation 157800
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 311815
98.0%
2 2539
 
0.8%
1 1250
 
0.4%
3 1246
 
0.4%
9 1246
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475896
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 311815
65.5%
. 157800
33.2%
2 2539
 
0.5%
1 1250
 
0.3%
3 1246
 
0.3%
9 1246
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 311815
65.5%
. 157800
33.2%
2 2539
 
0.5%
1 1250
 
0.3%
3 1246
 
0.3%
9 1246
 
0.3%

mqtt.msg_decoded_as
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:22.485871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:22.629151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

mqtt.msg
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

mqtt.msgtype
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
152737 
2.0
 
1289
14.0
 
1278
1.0
 
1250
3.0
 
1246

Length

Max length4
Median length3
Mean length3.0080989
Min length3

Characters and Unicode

Total characters474678
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 152737
96.8%
2.0 1289
 
0.8%
14.0 1278
 
0.8%
1.0 1250
 
0.8%
3.0 1246
 
0.8%

Length

2023-10-09T21:36:22.769395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:22.905675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 152737
96.8%
2.0 1289
 
0.8%
14.0 1278
 
0.8%
1.0 1250
 
0.8%
3.0 1246
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 310537
65.4%
. 157800
33.2%
1 2528
 
0.5%
2 1289
 
0.3%
4 1278
 
0.3%
3 1246
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316878
66.8%
Other Punctuation 157800
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 310537
98.0%
1 2528
 
0.8%
2 1289
 
0.4%
4 1278
 
0.4%
3 1246
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 474678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 310537
65.4%
. 157800
33.2%
1 2528
 
0.5%
2 1289
 
0.3%
4 1278
 
0.3%
3 1246
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 310537
65.4%
. 157800
33.2%
1 2528
 
0.5%
2 1289
 
0.3%
4 1278
 
0.3%
3 1246
 
0.3%

mqtt.proto_len
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156550 
4.0
 
1250

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156550
99.2%
4.0 1250
 
0.8%

Length

2023-10-09T21:36:23.048845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:23.181386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156550
99.2%
4.0 1250
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
4 1250
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314350
99.6%
4 1250
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
4 1250
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
4 1250
 
0.3%

mqtt.protoname
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

mqtt.topic
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 MiB

mqtt.topic_len
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156554 
24.0
 
1246

Length

Max length4
Median length3
Mean length3.0078961
Min length3

Characters and Unicode

Total characters474646
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156554
99.2%
24.0 1246
 
0.8%

Length

2023-10-09T21:36:23.353652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:23.492844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156554
99.2%
24.0 1246
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 314354
66.2%
. 157800
33.2%
2 1246
 
0.3%
4 1246
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316846
66.8%
Other Punctuation 157800
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314354
99.2%
2 1246
 
0.4%
4 1246
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 474646
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314354
66.2%
. 157800
33.2%
2 1246
 
0.3%
4 1246
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314354
66.2%
. 157800
33.2%
2 1246
 
0.3%
4 1246
 
0.3%

mqtt.ver
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156550 
4.0
 
1250

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156550
99.2%
4.0 1250
 
0.8%

Length

2023-10-09T21:36:23.649438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:23.773436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156550
99.2%
4.0 1250
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
4 1250
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314350
99.6%
4 1250
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
4 1250
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314350
66.4%
. 157800
33.3%
4 1250
 
0.3%

mbtcp.len
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:23.903908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:24.022813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

mbtcp.trans_id
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:24.137811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:24.265913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

mbtcp.unit_id
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
157800 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters473400
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157800
100.0%

Length

2023-10-09T21:36:24.405105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:24.533715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157800
100.0%

Most occurring characters

ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 315600
66.7%
Other Punctuation 157800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 315600
100.0%
Other Punctuation
ValueCountFrequency (%)
. 157800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 315600
66.7%
. 157800
33.3%

Attack_label
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
133499 
0
24301 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters157800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 133499
84.6%
0 24301
 
15.4%

Length

2023-10-09T21:36:24.677474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T21:36:24.853416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 133499
84.6%
0 24301
 
15.4%

Most occurring characters

ValueCountFrequency (%)
1 133499
84.6%
0 24301
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 157800
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 133499
84.6%
0 24301
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 157800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 133499
84.6%
0 24301
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 133499
84.6%
0 24301
 
15.4%

Attack_type
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Normal
24301 
DDoS_UDP
14498 
DDoS_ICMP
14090 
Ransomware
10925 
DDoS_HTTP
10561 
Other values (10)
83425 

Length

Max length21
Median length14
Mean length9.2164512
Min length3

Characters and Unicode

Total characters1454356
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMITM
2nd rowMITM
3rd rowMITM
4th rowMITM
5th rowMITM

Common Values

ValueCountFrequency (%)
Normal 24301
15.4%
DDoS_UDP 14498
9.2%
DDoS_ICMP 14090
8.9%
Ransomware 10925
 
6.9%
DDoS_HTTP 10561
 
6.7%
SQL_injection 10311
 
6.5%
Uploading 10269
 
6.5%
DDoS_TCP 10247
 
6.5%
Backdoor 10195
 
6.5%
Vulnerability_scanner 10076
 
6.4%
Other values (5) 32327
20.5%

Length

2023-10-09T21:36:25.059589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal 24301
15.4%
ddos_udp 14498
9.2%
ddos_icmp 14090
8.9%
ransomware 10925
 
6.9%
ddos_http 10561
 
6.7%
sql_injection 10311
 
6.5%
uploading 10269
 
6.5%
ddos_tcp 10247
 
6.5%
backdoor 10195
 
6.5%
vulnerability_scanner 10076
 
6.4%
Other values (5) 32327
20.5%

Most occurring characters

ValueCountFrequency (%)
o 145652
 
10.0%
D 113290
 
7.8%
a 106827
 
7.3%
n 105260
 
7.2%
S 89882
 
6.2%
r 87635
 
6.0%
_ 79854
 
5.5%
P 69456
 
4.8%
i 64117
 
4.4%
l 54722
 
3.8%
Other values (28) 537661
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 890632
61.2%
Uppercase Letter 483870
33.3%
Connector Punctuation 79854
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 145652
16.4%
a 106827
12.0%
n 105260
11.8%
r 87635
9.8%
i 64117
 
7.2%
l 54722
 
6.1%
e 42389
 
4.8%
s 40979
 
4.6%
c 40653
 
4.6%
m 35226
 
4.0%
Other values (10) 167172
18.8%
Uppercase Letter
ValueCountFrequency (%)
D 113290
23.4%
S 89882
18.6%
P 69456
14.4%
T 32583
 
6.7%
U 24767
 
5.1%
C 24337
 
5.0%
N 24301
 
5.0%
M 16518
 
3.4%
I 15304
 
3.2%
R 10925
 
2.3%
Other values (7) 62507
12.9%
Connector Punctuation
ValueCountFrequency (%)
_ 79854
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1374502
94.5%
Common 79854
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 145652
 
10.6%
D 113290
 
8.2%
a 106827
 
7.8%
n 105260
 
7.7%
S 89882
 
6.5%
r 87635
 
6.4%
P 69456
 
5.1%
i 64117
 
4.7%
l 54722
 
4.0%
e 42389
 
3.1%
Other values (27) 495272
36.0%
Common
ValueCountFrequency (%)
_ 79854
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1454356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 145652
 
10.0%
D 113290
 
7.8%
a 106827
 
7.3%
n 105260
 
7.2%
S 89882
 
6.2%
r 87635
 
6.0%
_ 79854
 
5.5%
P 69456
 
4.8%
i 64117
 
4.4%
l 54722
 
3.8%
Other values (28) 537661
37.0%

Interactions

2023-10-09T21:35:57.824184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:09.110705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:13.845050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:17.376915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:20.380001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:23.085205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:26.951984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:29.777527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:32.619311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:35.594569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:40.516230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:44.578131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:47.475960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:49.974136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:52.292703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:54.896051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:58.008018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:09.381322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:14.127045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:17.565617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:20.541000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:23.259109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:27.137946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:29.971016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:32.788327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:35.841932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:41.025009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:44.833491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:47.695132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:50.125069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:52.440377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:55.067826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:58.174962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:09.567136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-10-09T21:35:32.292407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:35.109385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:40.037121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:44.027370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:47.071113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:49.684144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:52.008861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:54.520324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:57.418871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:36:00.918252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:13.531213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:17.191377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:20.221210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:22.900952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:26.765991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:29.610268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:32.459794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:35.304304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:40.306129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:44.308147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:47.271685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:49.826228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:52.153005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:54.735994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T21:35:57.614183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T21:36:25.242848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
icmp.checksumicmp.seq_leicmp.transmit_timestamphttp.content_lengthtcp.acktcp.ack_rawtcp.checksumtcp.dstporttcp.flagstcp.lentcp.sequdp.portudp.streamudp.time_deltadns.qry.namedns.qry.quarp.opcodearp.hw.sizehttp.responsetcp.connection.fintcp.connection.rsttcp.connection.syntcp.connection.synacktcp.flags.ackdns.retransmissiondns.retransmit_requestmqtt.conflag.cleansessmqtt.conflagsmqtt.hdrflagsmqtt.lenmqtt.msgtypemqtt.proto_lenmqtt.topic_lenmqtt.verAttack_labelAttack_type
icmp.checksum1.0000.9310.071-0.081-0.395-0.393-0.437-0.441-0.444-0.190-0.361-0.016-0.102-0.016-0.033-0.0130.0200.0290.0660.0750.0970.1150.0530.3990.0000.0000.0260.0260.0260.0260.0260.0260.0260.0260.1290.312
icmp.seq_le0.9311.000-0.008-0.084-0.410-0.408-0.453-0.457-0.460-0.197-0.374-0.017-0.011-0.0170.258-0.0130.0210.0300.0680.0770.1010.1200.0540.4130.0000.0000.0270.0270.0270.0270.0270.0270.0270.0270.1330.301
icmp.transmit_timestamp0.071-0.0081.000-0.006-0.028-0.028-0.031-0.032-0.032-0.014-0.026-0.001-0.007-0.001-0.002-0.0010.0000.0000.0040.0040.0060.0080.0020.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.287
http.content_length-0.081-0.084-0.0061.0000.2520.1680.0940.1080.3480.4660.303-0.013-0.080-0.013-0.026-0.0100.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.002
tcp.ack-0.395-0.410-0.0280.2521.0000.6340.4630.5010.4430.2940.465-0.063-0.393-0.063-0.127-0.0500.0160.0240.0540.0620.0810.6560.0430.3310.0000.0000.0210.0210.0220.0210.0220.0210.0210.0210.1070.220
tcp.ack_raw-0.393-0.408-0.0280.1680.6341.0000.4600.5610.6600.3010.585-0.062-0.392-0.062-0.127-0.0500.0910.1300.1530.1680.1810.2580.1190.7660.0070.0000.0650.0650.0600.0610.0600.0650.0560.0650.1870.301
tcp.checksum-0.437-0.453-0.0310.0940.4630.4601.0000.5160.5190.2110.418-0.069-0.435-0.069-0.141-0.0550.1100.1560.0930.1020.1380.1650.0760.5610.0100.0000.0380.0380.0380.0380.0380.0380.0370.0380.1760.254
tcp.dstport-0.441-0.457-0.0320.1080.5010.5610.5161.0000.5970.1520.520-0.070-0.439-0.070-0.142-0.0560.0530.0750.4450.1200.5460.2810.2720.4820.0000.0000.0660.0660.1060.1170.1060.0660.0660.0660.3420.348
tcp.flags-0.444-0.460-0.0320.3480.4430.6600.5190.5971.0000.5860.743-0.070-0.442-0.070-0.143-0.0560.0980.1390.4210.2930.8670.5300.5211.0000.0100.0000.1720.1720.1750.1740.1750.1720.1720.1720.3530.512
tcp.len-0.190-0.197-0.0140.4660.2940.3010.2110.1520.5861.0000.4730.099-0.1890.099-0.0220.0760.0000.0000.0000.0020.0050.0070.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.124
tcp.seq-0.361-0.374-0.0260.3030.4650.5850.4180.5200.7430.4731.000-0.018-0.359-0.018-0.104-0.0150.0050.0100.0270.0310.0400.0480.0210.0960.0000.0000.0080.0080.0090.0090.0090.0080.0080.0080.2980.099
udp.port-0.016-0.017-0.001-0.013-0.063-0.062-0.069-0.070-0.0700.099-0.0181.000-0.0011.0000.3040.7950.0000.0000.0000.0000.0000.0000.0000.0160.5090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.009
udp.stream-0.102-0.011-0.007-0.080-0.393-0.392-0.435-0.439-0.442-0.189-0.359-0.0011.000-0.0010.2500.0020.0190.0270.0620.0700.0910.1080.0490.3750.0000.0000.0240.0240.0250.0250.0250.0240.0240.0240.1210.297
udp.time_delta-0.016-0.017-0.001-0.013-0.063-0.062-0.069-0.070-0.0700.099-0.0181.000-0.0011.0000.3040.7940.0000.0000.0000.0040.0080.0110.0000.0450.6000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.115
dns.qry.name-0.0330.258-0.002-0.026-0.127-0.127-0.141-0.142-0.143-0.022-0.1040.3040.2500.3041.0000.3730.0000.0050.0180.0210.0280.0340.0140.1190.0000.0000.0030.0030.0030.0030.0030.0030.0030.0030.0380.094
dns.qry.qu-0.013-0.013-0.001-0.010-0.050-0.050-0.055-0.056-0.0560.076-0.0150.7950.0020.7940.3731.0000.0000.0000.0030.0050.0090.0120.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.139
arp.opcode0.0200.0210.0000.0000.0160.0910.1100.0530.0980.0000.0050.0000.0190.0000.0000.0001.0001.0000.0220.0250.0320.0380.0170.1320.0000.0000.0080.0080.0120.0100.0120.0080.0080.0080.0360.157
arp.hw.size0.0290.0300.0000.0000.0240.1300.1560.0750.1390.0000.0100.0000.0270.0000.0050.0001.0001.0000.0220.0250.0320.0380.0170.1320.0000.0000.0080.0080.0180.0150.0180.0080.0080.0080.0350.220
http.response0.0660.0680.0040.0050.0540.1530.0930.4450.4210.0000.0270.0000.0620.0000.0180.0030.0220.0221.0000.0530.0700.0840.0380.1660.0000.0000.0190.0190.0400.0340.0400.0190.0190.0190.0930.490
tcp.connection.fin0.0750.0770.0040.0000.0620.1680.1020.1200.2930.0020.0310.0000.0700.0040.0210.0050.0250.0250.0531.0000.0800.0950.0440.1880.0000.0000.0220.0220.3650.0390.3650.0220.0220.0220.0890.283
tcp.connection.rst0.0970.1010.0060.0000.0810.1810.1380.5460.8670.0050.0400.0000.0910.0080.0280.0090.0320.0320.0700.0801.0000.1230.0570.0880.0000.0000.0290.0290.0580.0500.0580.0290.0290.0290.0080.473
tcp.connection.syn0.1150.1200.0080.0000.6560.2580.1650.2810.5300.0070.0480.0000.1080.0110.0340.0120.0380.0380.0840.0950.1231.0000.0670.5050.0030.0000.0340.0340.0700.0600.0700.0340.0340.0340.1020.498
tcp.connection.synack0.0530.0540.0020.0000.0430.1190.0760.2720.5210.0000.0210.0000.0490.0000.0140.0000.0170.0170.0380.0440.0570.0671.0000.1330.0000.0000.0150.0150.0320.0270.0320.0150.0150.0150.0610.196
tcp.flags.ack0.3990.4130.0300.0000.3310.7660.5610.4821.0000.0290.0960.0160.3750.0450.1190.0480.1320.1320.1660.1880.0880.5050.1331.0000.0170.0000.0680.0680.1380.1190.1380.0680.0670.0680.1870.719
dns.retransmission0.0000.0000.0000.0000.0000.0070.0100.0000.0100.0000.0000.5090.0000.6000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0171.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.016
dns.retransmit_request0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mqtt.conflag.cleansess0.0260.0270.0000.0000.0210.0650.0380.0660.1720.0000.0080.0000.0240.0000.0030.0000.0080.0080.0190.0220.0290.0340.0150.0680.0000.0001.0001.0001.0001.0001.0001.0000.0071.0000.2090.209
mqtt.conflags0.0260.0270.0000.0000.0210.0650.0380.0660.1720.0000.0080.0000.0240.0000.0030.0000.0080.0080.0190.0220.0290.0340.0150.0680.0000.0001.0001.0001.0001.0001.0001.0000.0071.0000.2090.209
mqtt.hdrflags0.0260.0270.0000.0000.0220.0600.0380.1060.1750.0000.0090.0000.0250.0000.0030.0000.0120.0180.0400.3650.0580.0700.0320.1380.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.4270.213
mqtt.len0.0260.0270.0000.0000.0210.0610.0380.1170.1740.0000.0090.0000.0250.0000.0030.0000.0100.0150.0340.0390.0500.0600.0270.1190.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.3670.212
mqtt.msgtype0.0260.0270.0000.0000.0220.0600.0380.1060.1750.0000.0090.0000.0250.0000.0030.0000.0120.0180.0400.3650.0580.0700.0320.1380.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.4270.213
mqtt.proto_len0.0260.0270.0000.0000.0210.0650.0380.0660.1720.0000.0080.0000.0240.0000.0030.0000.0080.0080.0190.0220.0290.0340.0150.0680.0000.0001.0001.0001.0001.0001.0001.0000.0071.0000.2090.209
mqtt.topic_len0.0260.0270.0000.0000.0210.0560.0370.0660.1720.0000.0080.0000.0240.0000.0030.0000.0080.0080.0190.0220.0290.0340.0150.0670.0000.0000.0070.0071.0001.0001.0000.0071.0000.0070.2090.209
mqtt.ver0.0260.0270.0000.0000.0210.0650.0380.0660.1720.0000.0080.0000.0240.0000.0030.0000.0080.0080.0190.0220.0290.0340.0150.0680.0000.0001.0001.0001.0001.0001.0001.0000.0071.0000.2090.209
Attack_label0.1290.1330.0090.0000.1070.1870.1760.3420.3530.0080.2980.0290.1210.0330.0380.0140.0360.0350.0930.0890.0080.1020.0610.1870.0310.0000.2090.2090.4270.3670.4270.2090.2090.2091.0001.000
Attack_type0.3120.3010.2870.0020.2200.3010.2540.3480.5120.1240.0990.0090.2970.1150.0940.1390.1570.2200.4900.2830.4730.4980.1960.7190.0160.0000.2090.2090.2130.2120.2130.2090.2090.2091.0001.000

Missing values

2023-10-09T21:36:01.631494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T21:36:03.798936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

frame.timeip.src_hostip.dst_hostarp.dst.proto_ipv4arp.opcodearp.hw.sizearp.src.proto_ipv4icmp.checksumicmp.seq_leicmp.transmit_timestampicmp.unusedhttp.file_datahttp.content_lengthhttp.request.uri.queryhttp.request.methodhttp.refererhttp.request.full_urihttp.request.versionhttp.responsehttp.tls_porttcp.acktcp.ack_rawtcp.checksumtcp.connection.fintcp.connection.rsttcp.connection.syntcp.connection.synacktcp.dstporttcp.flagstcp.flags.acktcp.lentcp.optionstcp.payloadtcp.seqtcp.srcportudp.portudp.streamudp.time_deltadns.qry.namedns.qry.name.lendns.qry.qudns.qry.typedns.retransmissiondns.retransmit_requestdns.retransmit_request_inmqtt.conack.flagsmqtt.conflag.cleansessmqtt.conflagsmqtt.hdrflagsmqtt.lenmqtt.msg_decoded_asmqtt.msgmqtt.msgtypemqtt.proto_lenmqtt.protonamemqtt.topicmqtt.topic_lenmqtt.vermbtcp.lenmbtcp.trans_idmbtcp.unit_idAttack_labelAttack_type
06.0192.168.0.1520.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
16.0192.168.0.1010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
26.0192.168.0.1520.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
36.0192.168.0.1010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
46.0192.168.0.1520.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
56.0192.168.0.1010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
66.0192.168.0.1520.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
76.0192.168.0.1010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
80.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
90.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM
frame.timeip.src_hostip.dst_hostarp.dst.proto_ipv4arp.opcodearp.hw.sizearp.src.proto_ipv4icmp.checksumicmp.seq_leicmp.transmit_timestampicmp.unusedhttp.file_datahttp.content_lengthhttp.request.uri.queryhttp.request.methodhttp.refererhttp.request.full_urihttp.request.versionhttp.responsehttp.tls_porttcp.acktcp.ack_rawtcp.checksumtcp.connection.fintcp.connection.rsttcp.connection.syntcp.connection.synacktcp.dstporttcp.flagstcp.flags.acktcp.lentcp.optionstcp.payloadtcp.seqtcp.srcportudp.portudp.streamudp.time_deltadns.qry.namedns.qry.name.lendns.qry.qudns.qry.typedns.retransmissiondns.retransmit_requestdns.retransmit_request_inmqtt.conack.flagsmqtt.conflag.cleansessmqtt.conflagsmqtt.hdrflagsmqtt.lenmqtt.msg_decoded_asmqtt.msgmqtt.msgtypemqtt.proto_lenmqtt.protonamemqtt.topicmqtt.topic_lenmqtt.vermbtcp.lenmbtcp.trans_idmbtcp.unit_idAttack_labelAttack_type
1577902021 23:24:32.6467400005.78.209.48192.168.0.12800.00.0051295.039144.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577912021 23:24:32.651986000157.210.125.7192.168.0.12800.00.0024415.039249.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577922021 23:24:32.674477000221.40.206.7192.168.0.12800.00.0020573.039776.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577932021 23:24:32.68559900095.70.146.72192.168.0.12800.00.0019548.040036.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577942021 23:24:32.6910220005.153.51.123192.168.0.12800.00.0039515.040214.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577952021 23:24:32.698981000193.152.82.43192.168.0.12800.00.0048729.040690.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577962021 23:24:32.699354000253.52.1.213192.168.0.12800.00.0045657.040702.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577972021 23:24:32.719931000107.155.221.49192.168.0.12800.00.0057686.041423.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577982021 23:24:32.75205400077.242.58.228192.168.0.12800.00.009555.042379.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP
1577992021 23:24:32.780376000149.40.90.151192.168.0.12800.00.0035144.045095.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01DDoS_ICMP

Duplicate rows

Most frequently occurring

frame.timeip.src_hostip.dst_hostarp.opcodearp.hw.sizeicmp.checksumicmp.seq_leicmp.transmit_timestampicmp.unusedhttp.content_lengthhttp.responsehttp.tls_porttcp.acktcp.ack_rawtcp.checksumtcp.connection.fintcp.connection.rsttcp.connection.syntcp.connection.synacktcp.dstporttcp.flagstcp.flags.acktcp.lentcp.sequdp.portudp.streamudp.time_deltadns.qry.namedns.qry.qudns.qry.typedns.retransmissiondns.retransmit_requestdns.retransmit_request_inmqtt.conflag.cleansessmqtt.conflagsmqtt.hdrflagsmqtt.lenmqtt.msg_decoded_asmqtt.msgtypemqtt.proto_lenmqtt.topic_lenmqtt.vermbtcp.lenmbtcp.trans_idmbtcp.unit_idAttack_labelAttack_type# duplicates
00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM480
396.0192.168.0.1010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM155
416.0192.168.0.1520.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM120
376.00.0.0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM58
406.0192.168.0.1280.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM4
10.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.05353.00.021.00.0255.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM2
20.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.049436.00.015.00.0255.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM2
30.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.049893.00.015.00.0255.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM2
40.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.049909.00.015.00.0255.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM2
50.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.050566.00.015.00.0255.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01MITM2